torch DataLoader's pin_memory was never set, defaulting to False. Setting
pin_memory=True allocates batch tensors in page-locked memory, which lets
PyTorch issue async cuda host->device copies via DMA - typically 10-30%
faster transfer for image/video training tensors and can reduce per-step
overhead noticeably on Windows where num_workers is forced to 0 (see
6d31c6db / #770) and the main process pays the full transfer cost.
pin_memory is orthogonal to num_workers and is safe with num_workers=0,
so this benefits Windows/macOS users (who currently can't use multi-worker
loading due to pickling constraints) without changing any other behavior.
Gated on torch.cuda.is_available() so it's a no-op on CPU/MPS/ROCm-without
-CUDA-API setups.
Refs #758.
Booyaka101
67e223b502